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by io.github.pipeworx-io

Polymarket Fill Risk

polymarket_fill_risk
Read-onlyIdempotent

Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of market (single-market mode) or event (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNoSingle-market: buy_yes | sell_yes | buy_no | sell_no (default buy_yes). Basket: sell_yes | buy_yes (default auto — sell if partition sum > 1, buy if < 1).
eventNoBasket mode: event slug or full polymarket.com URL — checks every leg of the partition.
marketNoSingle-market mode: market slug or full polymarket.com URL.
size_usdNoSingle-market: USD to spend (buys) or target proceeds (sells). Basket: settlement notional — shares per leg, each paying $1 at resolution. Default 1000, clamp 10–1,000,000.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: walks the order book ladder, returns fill details and a verdict (clean|degraded|cannot_fill), warns about forced_directional_risk, and explains that partial basket fills convert an arb into an unhedged directional position. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat lengthy but well-structured with clear sections for single-market and basket modes, and includes a critical usage warning. It is front-loaded with the primary purpose. Every sentence adds value, though it could be slightly more concise without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (two modes, multiple return fields, no output schema), the description is comprehensive. It explains both modes, the return values, the rationale for usage, and potential risks. An agent has all necessary information to correctly invoke this tool in the intended context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description adds meaning beyond the schema: it explains the default behavior for 'side' (auto for basket), clarifies the interpretation of 'size_usd' for each mode (max spend vs target proceeds vs settlement notional), and provides usage context for event and market parameters. This significantly aids correct parameter selection.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes two modes (single-market and basket) and lists specific outputs (top_of_book, vwap_fill_price, slippage_pp, etc.), effectively differentiating it from sibling tools like polymarket_arbitrage and polymarket_edges.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains the rationale—theoretical overround on thin books is not capturable and partial fills cause directional risk—and provides clear prerequisites (requires market or event).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation3/5

While many tools have detailed descriptions that help differentiate them, there is significant overlap among query tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research. The prediction market tools also cluster together, making it challenging for an agent to quickly pick the right one without careful reading.

Naming Consistency4/5

Most tools follow a descriptive snake_case convention (e.g., ask_pipeworx, entity_profile, compare_entities). Minor deviations exist, such as 'ai_visibility_check' and 'deep_research', but overall the naming pattern is predictable and clear.

Tool Count3/5

With 33 tools, the server is larger than typical single-domain servers. While it supports a broad data platform, this count feels somewhat bloated and could benefit from consolidation, especially among overlapping query tools.

Completeness1/5

The server is named 'Materials' but contains only two materials-specific tools (materials_search, materials_stability). The remaining 31 tools cover unrelated domains (finance, economics, prediction markets, etc.), leaving the stated domain severely incomplete.

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